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Data Visualization with R and ggplot2

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Data Visualization with R and ggplot2, available at $64.99, has an average rating of 4.15, with 109 lectures, based on 260 reviews, and has 1992 subscribers.

You will learn about Visualize data Foundations of data visualization (Grammar of Graphics and ggplot2) Transform data before visualization is applied (data wrangling libraries) Apply exploratory data analysis techniques with R and ggplot2 Wrap up analysis using RMarkdown reports Use ggplot2 for creating many different standard statistical plots This course is ideal for individuals who are Anyone who is interested in data analysis or data visualization or Aspiring data scientists, statisticians or data (business) analysts or Anyone who would like to impress his/her boss or coworkers with amazing data visualizations or Anyone whose job, research or hobby is related to visualizing data or Anyone whose work is related with data presentation or extracting insights from the data or Students working with data It is particularly useful for Anyone who is interested in data analysis or data visualization or Aspiring data scientists, statisticians or data (business) analysts or Anyone who would like to impress his/her boss or coworkers with amazing data visualizations or Anyone whose job, research or hobby is related to visualizing data or Anyone whose work is related with data presentation or extracting insights from the data or Students working with data.

Enroll now: Data Visualization with R and ggplot2

Summary

Title: Data Visualization with R and ggplot2

Price: $64.99

Average Rating: 4.15

Number of Lectures: 109

Number of Published Lectures: 109

Number of Curriculum Items: 109

Number of Published Curriculum Objects: 109

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

  • Visualize data
  • Foundations of data visualization (Grammar of Graphics and ggplot2)
  • Transform data before visualization is applied (data wrangling libraries)
  • Apply exploratory data analysis techniques with R and ggplot2
  • Wrap up analysis using RMarkdown reports
  • Use ggplot2 for creating many different standard statistical plots

Who Should Attend

  • Anyone who is interested in data analysis or data visualization
  • Aspiring data scientists, statisticians or data (business) analysts
  • Anyone who would like to impress his/her boss or coworkers with amazing data visualizations
  • Anyone whose job, research or hobby is related to visualizing data
  • Anyone whose work is related with data presentation or extracting insights from the data
  • Students working with data

Target Audiences

  • Anyone who is interested in data analysis or data visualization
  • Aspiring data scientists, statisticians or data (business) analysts
  • Anyone who would like to impress his/her boss or coworkers with amazing data visualizations
  • Anyone whose job, research or hobby is related to visualizing data
  • Anyone whose work is related with data presentation or extracting insights from the data
  • Students working with data

Today we live in a world where tons of data is generated every second. We need to analyze datato get some useful insight. One of the strongest weapons for data insight is data visualization. Probably you have heard this one before: “A picture tells more than a thousand words combined “. Therefore to tell stories from the data we need tools for producing adequate and amazing graphics.  Here R as one of the most rapidly growing tools in the fields of data scienceand statisticsprovides needed assistance. If you combine Rwith its library ggplot2 you get one of the deadliest toolsfor data visualization, which grows every dayand is freely accessibleto anyone.

This course is designed to first give you quick and proper theoretical foundationsfor creating statistical plots. Then you dive into the world of exploratory data analysiswhere you are confronted with different datasetsand creatinga wide varietyof statistical plots.

If you take this course, you will learn a ton of new things.  Here are just a few topicsyou will be engaged with:

  • The grammar of graphics (the idea behind statistical plots, the foundation of ggplot2)

  • Data transformation with dplyrand tidyr(crash course included)

  • Exploratory data analysis(EDA) (statistical plots for exploring one continuous or one discrete variable)

  • EDAfor exploring twoor more variables(different statistical plots)

  • Combine ggplot2with RMarkdownto wrap up your analysis and produce HTML reports

  • Create some additional typesof plotsbycombining ggplot2 and supplementary libraries (word cloud, parallel coordinates plot, heat map, radar plot, …)

  • Draw mapsto show the spreadof coronavirus disease

  • Customize the plot’s theme

  • Create subplotsusing cowplotlibrary

  • Highlight dataon your plot with gghighlightlibrary

  • and much more…

Course includes:

  • over 20 hoursof lecture videos,

  • R scripts and additional data(provided in the course material),

  • engagement with assignments, where you have to test your skills,

  • assignments walkthrough videos(where you can check your results).

All being said this makes one of Udemy’s most comprehensive coursesfor data visualizationusing Rand ggplot2.

Enroll today and become the master of data visualization!!!

Course Curriculum

Chapter 1: Course intro

Lecture 1: Course intro

Chapter 2: ggplot2 foundations

Lecture 1: Section intro

Lecture 2: Grammar of graphics and ggplot2

Lecture 3: Data – part 1

Lecture 4: Data – part 2

Lecture 5: Aesthetics – Mapping

Lecture 6: Geometries

Lecture 7: Facets

Lecture 8: Statistics

Lecture 9: Coordinates and Scales

Lecture 10: Theme

Lecture 11: Export plot

Lecture 12: Section summary and assignment

Lecture 13: Assignment walkthrough

Chapter 3: Data wrangling crash course

Lecture 1: Section intro

Lecture 2: Data transformation libraries

Lecture 3: Variables manipulation

Lecture 4: Cases manipulation

Lecture 5: Summarising and grouping

Lecture 6: Piping

Lecture 7: Pivoting

Lecture 8: Separating and uniting

Lecture 9: Transform and visualize data

Lecture 10: Section summary and assignment

Lecture 11: Assignment walkthrough

Chapter 4: Exploratory data analysis

Lecture 1: Section intro

Lecture 2: Exploratory data analysis

Lecture 3: Diamonds dataset

Lecture 4: Dotplot

Lecture 5: Histogram and density plot

Lecture 6: Frequency polygon

Lecture 7: Area plot

Lecture 8: Bar plot

Lecture 9: Section summary and assignment

Lecture 10: Assignment walkthrough – part 1

Lecture 11: Assignment walkthrough – part 2

Chapter 5: Explore two variables

Lecture 1: Section intro

Lecture 2: Scatterplot

Lecture 3: Smoothing line and transformed axes

Lecture 4: Rug plot

Lecture 5: Continuous bivariate distribution

Lecture 6: Boxplot

Lecture 7: Violin plot

Lecture 8: Comparing two discrete variables

Lecture 9: Matrix plots

Lecture 10: Section summary and assignment

Lecture 11: Assignment walkthrough – part 1

Lecture 12: Assignment walkthrough – part 2

Chapter 6: Explore many variables

Lecture 1: Section intro

Lecture 2: Color described with continuous variable

Lecture 3: Color described with discrete variable

Lecture 4: Size and shape of points

Lecture 5: Facet wrap

Lecture 6: Facet grid

Lecture 7: Plots with many graphical elements

Lecture 8: Diamond price prediction model – part 1

Lecture 9: Diamond price prediction model – part 2

Lecture 10: Diamond price prediction model – part 3

Lecture 11: Section summary and assignment

Lecture 12: Assignment walkthrough

Chapter 7: Analysis wrap up with RMarkdown

Lecture 1: Section intro

Lecture 2: RMarkdown

Lecture 3: Create section: The Dataset

Lecture 4: Create section: Exploratory Data Analysis

Lecture 5: Create subsection: Explore two variables

Lecture 6: Create subsection: Explore many variables

Lecture 7: Create section: Price prediction models

Lecture 8: html output customization

Lecture 9: Section summary and assignment

Lecture 10: Assignment walkthrough

Chapter 8: ggplot2 for standard plots and beyond

Lecture 1: Section intro

Lecture 2: Pie chart

Lecture 3: Donut chart

Lecture 4: Time series visualization

Lecture 5: Word cloud

Lecture 6: Waterfall chart

Lecture 7: Radar chart

Lecture 8: Parallel coordinates plot

Lecture 9: Heat map

Lecture 10: Mosaic plot

Lecture 11: Coronavirus dataset

Lecture 12: Create maps with ggplot2

Lecture 13: Section summary and assignment

Lecture 14: Assignment walkthrough – part 1

Lecture 15: Assignment walkthrough – part 2

Lecture 16: Assignment walkthrough – part 3

Chapter 9: Additional plot customization

Lecture 1: Section intro

Lecture 2: Custom themes – part 1

Lecture 3: Custom themes – part 2

Lecture 4: Annotations and text labels – part 1

Lecture 5: Annotations and text labels – part 2

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 7 votes
  • 3 stars: 17 votes
  • 4 stars: 92 votes
  • 5 stars: 142 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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